Search Results for author:
Found 25 papers, 13 papers with code
Outsourced Bayesian Optimization
This paper presents the outsourced-Gaussian process-upper confidence bound (O-GP-UCB) algorithm, which is the first algorithm for privacy-preserving Bayesian optimization (BO) in the outsourced setting with a provable performance guarantee.
Robustifying and Boosting Training-Free Neural Architecture Search
Nevertheless, the estimation ability of these metrics typically varies across different tasks, making it challenging to achieve robust and consistently good search performance on diverse tasks with only a single training-free metric.
Localized Zeroth-Order Prompt Optimization
Existing methodologies usually prioritize a global optimization for finding the global optimum, which however will perform poorly in certain tasks.
Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers
We perform instruction optimization for ChatGPT and use extensive experiments to show that our INSTINCT consistently outperforms the existing methods in different tasks, such as in various instruction induction tasks and the task of improving the zero-shot chain-of-thought instruction.
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data
The impressive performances of large language models (LLMs) and their immense potential for commercialization have given rise to serious concerns over the intellectual property (IP) of their training data.
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients
To this end, we (a) introduce trajectory-informed gradient surrogates which is able to use the history of function queries during optimization for accurate and query-efficient gradient estimation, and (b) develop the technique of adaptive gradient correction using these gradient surrogates to mitigate the aforementioned disparity.
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
To this end, we introduce our expected variance with Gaussian processes (EV-GP) criterion for neural active learning, which is theoretically guaranteed to select data points which lead to trained NNs with both (a) good predictive performances and (b) initialization robustness.
FedHQL: Federated Heterogeneous Q-Learning
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw trajectories.
Sample-Then-Optimize Batch Neural Thompson Sampling
linear model), which is equivalently sampled from the GP posterior with the NTK as the kernel function.
Bayesian Optimization under Stochastic Delayed Feedback
The existing BO methods assume that the function evaluation (feedback) is available to the learner immediately or after a fixed delay.
On Provably Robust Meta-Bayesian Optimization
We prove that both algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and show that RM-GP-UCB enjoys a better theoretical robustness than RM-GP-TS.
Federated Neural Bandits
To better exploit the federated setting, FN-UCB adopts a weighted combination of two UCBs: $\text{UCB}^{a}$ allows every agent to additionally use the observations from the other agents to accelerate exploration (without sharing raw observations), while $\text{UCB}^{b}$ uses an NN with aggregated parameters for reward prediction in a similar way to federated averaging for supervised learning.
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
To adapt the EI for better performance under cumulative regret, we introduce a novel quantity called the evaluation cost which is compared against the acquisition function, and with this, develop the expected improvement-cost (EIC) algorithm.
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search
As a consequence, (a) the relationships among these metrics are unclear, (b) there is no theoretical interpretation for their empirical performances, and (c) there may exist untapped potential in existing training-free NAS, which probably can be unveiled through a unified theoretical understanding.
Optimizing Conditional Value-At-Risk of Black-Box Functions
This paper presents two Bayesian optimization (BO) algorithms with theoretical performance guarantee to maximize the conditional value-at-risk (CVaR) of a black-box function: CV-UCB and CV-TS which are based on the well-established principle of optimism in the face of uncertainty and Thompson sampling, respectively.
Differentially Private Federated Bayesian Optimization with Distributed Exploration
The resulting differentially private FTS with DE (DP-FTS-DE) algorithm is endowed with theoretical guarantees for both the privacy and utility and is amenable to interesting theoretical insights about the privacy-utility trade-off.
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-making policy without sharing raw trajectories.
Neural Ensemble Search via Bayesian Sampling
Unfortunately, these NAS algorithms aim to select only one single well-performing architecture from their search spaces and thus have overlooked the capability of neural network ensemble (i. e., an ensemble of neural networks with diverse architectures) in achieving improved performance over a single final selected architecture.
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization
Recent years have witnessed a surging interest in Neural Architecture Search (NAS).
Value-at-Risk Optimization with Gaussian Processes
Value-at-risk (VaR) is an established measure to assess risks in critical real-world applications with random environmental factors.
Private Outsourced Bayesian Optimization
This paper presents the private-outsourced-Gaussian process-upper confidence bound (PO-GP-UCB) algorithm, which is the first algorithm for privacy-preserving Bayesian optimization (BO) in the outsourced setting with a provable performance guarantee.
Federated Bayesian Optimization via Thompson Sampling
Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions.
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2).
Implicit Posterior Variational Inference for Deep Gaussian Processes
This paper presents an implicit posterior variational inference (IPVI) framework for DGPs that can ideally recover an unbiased posterior belief and still preserve time efficiency.
Bayesian Optimization with Binary Auxiliary Information
This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary information of binary type that can be more cheaply obtained, such as in policy search for reinforcement learning and hyperparameter tuning of machine learning models with early stopping.
